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        <title>A Large Scale Quantitative Exploration of Modeling Strategies for Content Scoring</title>
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    <abstract>We explore various supervised learning strategies for automated scoring of content knowledge for a large corpus of 130 different content-based questions spanning four subject areas (Science, Math, English Language Arts, and Social Studies) and containing over 230,000 responses scored by human raters. Based on our analyses, we provide specific recommendations for content scoring. These are based on patterns observed across multiple questions and assessments and are, therefore, likely to generalize to other scenarios and prove useful to the community as automated content scoring becomes more popular in schools and classrooms.</abstract>
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%0 Conference Proceedings
%T A Large Scale Quantitative Exploration of Modeling Strategies for Content Scoring
%A Madnani, Nitin
%A Loukina, Anastassia
%A Cahill, Aoife
%Y Tetreault, Joel
%Y Burstein, Jill
%Y Leacock, Claudia
%Y Yannakoudakis, Helen
%S Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F madnani-etal-2017-large
%X We explore various supervised learning strategies for automated scoring of content knowledge for a large corpus of 130 different content-based questions spanning four subject areas (Science, Math, English Language Arts, and Social Studies) and containing over 230,000 responses scored by human raters. Based on our analyses, we provide specific recommendations for content scoring. These are based on patterns observed across multiple questions and assessments and are, therefore, likely to generalize to other scenarios and prove useful to the community as automated content scoring becomes more popular in schools and classrooms.
%R 10.18653/v1/W17-5052
%U https://aclanthology.org/W17-5052
%U https://doi.org/10.18653/v1/W17-5052
%P 457-467

Markdown (Informal)

[A Large Scale Quantitative Exploration of Modeling Strategies for Content Scoring](https://aclanthology.org/W17-5052) (Madnani et al., BEA 2017)

• A Large Scale Quantitative Exploration of Modeling Strategies for Content Scoring (Madnani et al., BEA 2017)

ACL

• Nitin Madnani, Anastassia Loukina, and Aoife Cahill. 2017. A Large Scale Quantitative Exploration of Modeling Strategies for Content Scoring. In Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications, pages 457–467, Copenhagen, Denmark. Association for Computational Linguistics.